Large Data Querying
Machine Learning and AI datasets are rapidly growing in size, type, and distribution. have By leveraging the architectural principles and features below, query engines such as Presto can effectively distribute computational load across multiple nodes, access and combine data from various distributed sources, and efficiently process large datasets in parallel, enabling interactive analytics on massive, heterogeneous data stores.
Massively Parallel Processing (MPP) Architecture
Query processing coordinator node and multiple worker nodes. This allows it to parallelize query execution across the worker nodes, enabling it to process large datasets in a distributed manner.
Separation of Compute and Storage
Separating the compute layer (query processing) from the storage layer (data sources) allows querying various data sources like Hadoop, AWS S3, databases, etc., without being tied to a specific storage system.
Connectors for Heterogeneous Data Sources
Connectors allow querying data from diverse sources such as HDFS, S3, MySQL, PostgreSQL, Cassandra, MongoDB, and more. This enables querying across large, distributed datasets residing in different storage systems within a single query.
In-Memory Processing
Unlike traditional SQL-on-Hadoop solutions like Hive, which write intermediate results to disk. data can be processed in memory, resulting in significant performance improvements for interactive queries on large datasets.
Efficient Query Execution
Various optimization techniques, such as cost-based optimization, to efficiently execute queries can be employed. Parallel tasks can be created and distributed across worker nodes, enabling more efficient processing of large datasets.
Scalability
Techniques such as these are designed to be highly scalable, allowing handling of petabyte-scale datasets by adding more worker nodes to the cluster. This supports fault-tolerance and load-balancing mechanisms to ensure reliable and efficient query execution.
Standard SQL Support
Supporting standard ANSI SQL, including complex queries, joins, subqueries, and aggregations, makes it accessible to users familiar with SQL, without the need to learn a new query language.